Deep Learning Regression with R

Last Update: January, 2018

1. Course Objective

Learn Deep Learning Regression main topics using R statistical software® in this practical course for all knowledge levels. Feel free to take a look at Course Curriculum.

2. Skills Learned

At the end of this course you will know how to:

  • Create target and predictor algorithm features (supervised regression deep learning task).
  • Select relevant predictor features subset through univariate filter methods (Student’s t-test, ANOVA F-test) and extract predictor features transformations (principal component analysis PCA, stacked autoencoders, restricted Boltzmann machines RBM and deep belief networks DBN).
  • Train algorithms such as artificial neural networks ANN, deep neural networks DNN and recurrent neural networks RNN.
  • Regularize algorithm learning (nodes connections weight decay, visible or hidden layers dropout fractions, stochastic gradient descent algorithm SGD learning rate).
  • Reduce recurrent neural network RNN vanishing gradient problem (long short-term memory LSTM units).
  • Test algorithms forecasting accuracy (mean absolute error, root mean squared error, mean absolute percentage error).

3. Typical Student

This course is ideal for you as:

  • Undergraduate or postgraduate who wants to learn about the subject.
  • Academic researcher who wishes to deepen your knowledge in data mining, applied statistical learning or artificial intelligence.
  • Business data scientist who desires to apply this knowledge in areas such as consumer analytics, finance, banking, health care, e-commerce or social media.